Biometric verification and identification methods of medical images can be used to find possible inconsistencies in patient records. Such methods may also be useful for forensic research. In this work we present a method for identifying patients by their hand radiographs. We use active appearance model representations presented before [1] to extract 64 shape features per bone from the metacarpals, the proximal, and the middle phalanges. The number of features was reduced to 20 by applying principal component analysis. Subsequently, a likelihood ratio classifier [2] determines whether an image potentially belongs to another patient in the data set. Firstly, to study the symmetry between both hands, we use a likelihood-ratio classifier to match 45 left hand images to a database of 44 (matching) right hand images and vice versa. We found an average equal error probability of 6.4%, which indicates that both hand shapes are highly symmetrical. Therefore, to increase the number of samples per patient, the distinction between left and right hands was omitted. Secondly, we did multiple experiments with randomly selected training images from 24 patients. For several patients there were multiple image pairs available. Test sets were created by using the images of three different patients and 10 other images from patients that were in the training set. We estimated the equal error rate at 0.05%. Our experiments suggest that the shapes of the hand bones contain biometric information that can be used to identify persons.